LAUSR

The accurate prediction of functional recovery after a stroke is essential for post-discharge treatment planning and resource utilization. Recently, machine learning (ML) algorithms with baseline clinical variables have demonstrated better performance for predicting the functional outcome of ischemic stroke compared with preexisting scoring systems developed by conventional statistics. However, most studies compared model performance by area under curve (AUC) only, and ML and conventional statistical approaches were not sufficiently evaluated in terms of the reliability and clinical utility. We aimed to compare the performance of the ML with that of the conventional logistic regression (LR) model by evaluating accuracy, reliability, and clinical utility using AUC comparison, calibration, and decision curve analysis to predict the outcome of a stroke using KOrean Stroke Neuroimaging Initiative (KOSNI) database. Using clinical variables measurable at admission (Supplementary methods 1), we used various ML algorithms including deep learning (DL), support vector machine (SVM), random forest (RF), XGboost (XGB), and conventional LR models for predicting 3-month modified Rankin Scale (mRS) >2 or 1 (Supplementary methods 2). Receiver operating characteristic (ROC) curve analysis was performed to evaluate the sensitivity and specificity of each model across each decision threshold. Calibration was evaluated using a reliability diagram and expected calibration error (ECE) to assess the reliability of estimates between the predicted and actual outcomes. The decision curve analysis was constructed to assess the clinical utility of various developed models (Supplementary methods 3). Six thousand seven hundred thirty-one patients included Reliability and Clinical Utility of Machine Learning to Predict Stroke Prognosis: Comparison with Logistic Regression